Fuzzy Logic Based Effective Clustering of Homogeneous Wireless Sensor Networks for Mobile Sink

Verma, Akshay; Kumar, Sunil; Gautam, Prateek Raj; Rashid, Tarique; Kumar, Arvind

doi:10.1109/jsen.2020.2969697

Cited by 80 publications

(52 citation statements)

References 16 publications

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“…However, obstacles are an integrated part in a practical scenario, and aforementioned cluster-head and treebased routing protocols become complicated to construct sink's tour. Moreover, a few work addresses data collection schedule in an obstructed network [35] [45]. In this consequence, to overcome the complexity of the data collection scheduling in a network containing obstacles, Xie and Pan introduced spanning graph-based data collection scheduling [35] of a mobile sink, in which they developed a heuristic path planning algorithm to skirt obstacles, as shown in Fig.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, rendezvous node selection and data collection approaches have been developing for obstructed-networks using artificial intelligence [44], fuzzy logic [45], and machine learning algorithms. In [44], Ghabel et al proposed DGOB algorithm for data collection over an obstructednetwork that was executed into two phases, cluster-and tour-constructions.…”

Section: Related Workmentioning

confidence: 99%

“…This approach exploited hierarchical agglomerative clustering, ant colony optimization, and genetic algorithms to construct clusters in the presence of obstacles. In [45], Verma et al developed fuzzy-logic based effective clustering (FLEC) that used three-tier communication-based approaches: nodes to cluster heads (CHs), CHs to super cluster heads (SCH), and then SCH to mobile sink. Both works [44] [45] exhibit higher computational complexity to select efficient rendezvous nodes in a large scale network.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, most of above works for obstructed-networks exhibit higher end-to-end data collection latency using a mobile sink due to scheduling periodically over a large circular-path [36] or cluster heads [37] [44] [45] to collect data. These works also violate the delay-deadline of realtime applications and increase energy consumption among the sensor nodes significantly.…”

Section: Related Workmentioning

confidence: 99%

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Lifetime Maximization of Sensor Networks Through Optimal Data Collection Scheduling of Mobile Sink

et al. 2020

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The problem of maximizing lifetime of a sensor network is still challenging mainly due to the stringent delay-deadline of real-time applications and heterogeneity of sensor devices. The problem is further complicated when the network contains many obstacles. In maximizing network lifetime, existing literature works either merely address issues of application delay-deadline and presence of obstacles, or analyze primitive data collection approaches for such an environment. In this paper, we formulate optimal data collection schedule of a mobile sink in an obstructed sensor network as a mixed-integer linear programming (MILP) problem. The proposed data collection scheduling finds an optimal set of rendezvous nodes over a preformed Starfish routing backbone, and corresponding sojourn duration so as to maximize the network lifetime while maintaining delay-deadline constraint in an obstructed network. The proposed Starfish-scheduling ensures a loop-free traveling path for a mobile sink across the network. The results of performance evaluation, performed in network simulator-2, depict the suitability of Starfish scheduling as it outperforms state-of-the-art-works in terms of extending network lifetime and data delivery throughput as well as reducing average end-to-end delay.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Lifetime Maximization of Sensor Networks Through Optimal Data Collection Scheduling of Mobile Sink

et al. 2020

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“…Sushil [14] proposes a quantum inspired green communication framework for energy balancing in sensor enabled IoT systems. Verma [15] proposes a fuzzy logic-based clustering algorithm for WSN to extend the network lifetime, by utilizing the concept of average energy-based probability and average threshold for appropriate cluster heads selection. Energy-efficient zone-based dual subsink protocol with dual cluster head was presented in [16], which is developed to schedule the data transmission of these relay nodes with mobile sink.…”

mentioning

confidence: 99%

Double sink energy hole avoidance strategy for wireless sensor network

Chen

Huang

Zhang

et al. 2020

J Wireless Com Network

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To solve the energy hole problem in wireless sensor networks with double sinks, a double sink energy hole avoidance strategy is proposed. The main idea is that two data sinks are set at fixed positions on both sides of the rectangular network to collect nodes data in the corresponding area of the network. In the network, sensor nodes are organized in non-uniform clusters. Clusters close to sink have a smaller cluster radius, and clusters far from sink have a larger cluster radius. According to the results of threshold training, monitoring area of double sink is dynamically adjusted based on the difference of energy consumption and load of nodes in the double sink monitoring area, so that the energy consumption load of nodes in the double sink monitoring area tends to be the same, so as to avoid the premature occurrence of energy hole phenomenon in the area with large load, leading to network failure. The experimental results demonstrate that the strategy proposed in this paper can efficiently balance the energy dissipation of double sink and prolong the network energy utilization efficiency and network lifetime.

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IoT based sensor network clustering for intelligent transportation system using meta‐heuristic algorithm

Malik,

Singh,

Manju

et al. 2024

Concurrency and Computation

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SummaryInternet of Things (IoT) based sensor networks have been established as a pillar in intelligent communication systems for efficiently handling roadside congestion and accidents. These IoT networks sense, collect, and process data on a real‐time basis. However, IoT based sensor network clustering has various energy constraints such as inefficient routing due to long‐haul transmission, hot spot problem, network overhead, and unstable network whenever deployed along with the roadside that affect their architecture. In such networks, clustering techniques play a crucial role in extending the lifespan and optimizing the routes by integrating sensor devices through clusters. Therefore, a meta‐heuristic algorithm for clustering in IoT sensor networks for an intelligent transportation system is proposed. In this work, the seagull optimization algorithm is applied for clustering by considering residual and average energy, node spacing, and distance fitness parameters. Moreover, this work also considers the dynamic communication range of the cluster heads for increasing the stability period and lifetime of the proposed networks. The experiment results demonstrate that the proposed Seagull optimization algorithm for clustering in IoT networks (SOAC‐IoTNs) and Seagull optimization algorithm for clustering in IoT networks with dynamic communication range (SOAC‐IoTNs‐DR) achieve a significant increase in the stability period and network lifetime, with percentage increments of 55.68% and 71.47%, and 10.03% and 88.66% respectively, compared to the existing optimized genetic algorithm for cluster head selection with single static sink (OptiGACHS‐StSS).

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Fuzzy Logic Based Effective Clustering of Homogeneous Wireless Sensor Networks for Mobile Sink

Cited by 80 publications

References 16 publications

Lifetime Maximization of Sensor Networks Through Optimal Data Collection Scheduling of Mobile Sink

Lifetime Maximization of Sensor Networks Through Optimal Data Collection Scheduling of Mobile Sink

Double sink energy hole avoidance strategy for wireless sensor network

IoT based sensor network clustering for intelligent transportation system using meta‐heuristic algorithm

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